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https://issues.apache.org/jira/browse/SPARK-17368?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15469564#comment-15469564
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Aris Vlasakakis commented on SPARK-17368:
-----------------------------------------
It goes from inconvenient to actually prohibitive in a practical sense. I have
a Dataset[Something], and inside case class Something I have various other case
classes, and somewhere inside there there is a particular value class. It is so
crazy to do manual unwrapping and rewrapping that at this point I just decided
to eat the performance cost and use a regular class, not value class (I removed
the 'extends AnyVal').
More generally, specially accommodating for value classes is *really hard* in a
practical setting because if I have a whole bunch of ADTs and other case
classes I'm working with, how do I know if anywhere in my domain I used a
*value class* and I suddenly have to jump through a bunch of hoops just so
Spark doesn't blow up? If I just had a Dataset[ThisIsAValueClass] with the
top-level class being a value class, what you're saying is easy, but in
practice the value class is one of many things somewhere deeper.
> Scala value classes create encoder problems and break at runtime
> ----------------------------------------------------------------
>
> Key: SPARK-17368
> URL: https://issues.apache.org/jira/browse/SPARK-17368
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 1.6.2, 2.0.0
> Environment: JDK 8 on MacOS
> Scala 2.11.8
> Spark 2.0.0
> Reporter: Aris Vlasakakis
>
> Using Scala value classes as the inner type for Datasets breaks in Spark 2.0
> and 1.6.X.
> This simple Spark 2 application demonstrates that the code will compile, but
> will break at runtime with the error. The value class is of course
> *FeatureId*, as it extends AnyVal.
> {noformat}
> Exception in thread "main" java.lang.RuntimeException: Error while encoding:
> java.lang.RuntimeException: Couldn't find v on int
> assertnotnull(input[0, int, true], top level non-flat input object).v AS v#0
> +- assertnotnull(input[0, int, true], top level non-flat input object).v
> +- assertnotnull(input[0, int, true], top level non-flat input object)
> +- input[0, int, true]".
> at
> org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:279)
> at
> org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:421)
> at
> org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:421)
> {noformat}
> Test code for Spark 2.0.0:
> {noformat}
> import org.apache.spark.sql.{Dataset, SparkSession}
> object BreakSpark {
> case class FeatureId(v: Int) extends AnyVal
> def main(args: Array[String]): Unit = {
> val seq = Seq(FeatureId(1), FeatureId(2), FeatureId(3))
> val spark = SparkSession.builder.getOrCreate()
> import spark.implicits._
> spark.sparkContext.setLogLevel("warn")
> val ds: Dataset[FeatureId] = spark.createDataset(seq)
> println(s"BREAK HERE: ${ds.count}")
> }
> }
> {noformat}
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